



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			A__	  =     ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

			A__	  =     (
			    ('layer.', 'layer_'),
			    ('word_embeddings.weight', 'word_embeddings'),
			    ('position_embeddings.weight', 'position_embeddings'),
			    ('token_type_embeddings.weight', 'token_type_embeddings'),
			    ('.', '/'),
			    ('LayerNorm/weight', 'LayerNorm/gamma'),
			    ('LayerNorm/bias', 'LayerNorm/beta'),
			    ('weight', 'kernel'),
			)

			if not os.path.isdir(UpperCAmelCase_       ):
						os.makedirs(UpperCAmelCase_       )

			A__	  =     model.state_dict()

			def to_tf_var_name(__UpperCamelCase       ):
						for patt, repl in iter(UpperCAmelCase_       ):
									A__	  =     name.replace(UpperCAmelCase_			,						UpperCAmelCase_       )
						return f'''bert/{name}'''

			def create_tf_var(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       ):
						A__	  =     tf.dtypes.as_dtype(tensor.dtype       )
						A__	  =     tf.get_variable(dtype=UpperCAmelCase_			,						shape=tensor.shape			,						name=UpperCAmelCase_			,						initializer=tf.zeros_initializer()       )
						session.run(tf.variables_initializer([tf_var]       )       )
						session.run(UpperCAmelCase_       )
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									A__	  =     to_tf_var_name(UpperCAmelCase_       )
									A__	  =     state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose       ):
												A__	  =     torch_tensor.T
									A__	  =     create_tf_var(tensor=UpperCAmelCase_			,						name=UpperCAmelCase_			,						session=UpperCAmelCase_       )
									tf.keras.backend.set_value(UpperCAmelCase_			,						UpperCAmelCase_       )
									A__	  =     session.run(UpperCAmelCase_       )
									print(f'''Successfully created {tf_name}: {np.allclose(UpperCAmelCase_			,						UpperCAmelCase_       )}'''       )

						A__	  =     tf.train.Saver(tf.trainable_variables()       )
						saver.save(UpperCAmelCase_			,						os.path.join(UpperCAmelCase_			,						model_name.replace('-'			,						'_'       ) + '.ckpt'       )       )


def A    (							__UpperCamelCase=None       )    ->   Tuple:
			A__	  =     argparse.ArgumentParser()
			parser.add_argument('--model_name'			,						type=UpperCAmelCase_			,						required=UpperCAmelCase_			,						help='model name e.g. bert-base-uncased'       )
			parser.add_argument(
			    '--cache_dir'			,						type=UpperCAmelCase_			,						default=UpperCAmelCase_			,						required=UpperCAmelCase_			,						help='Directory containing pytorch model'       )
			parser.add_argument('--pytorch_model_path'			,						type=UpperCAmelCase_			,						required=UpperCAmelCase_			,						help='/path/to/<pytorch-model-name>.bin'       )
			parser.add_argument('--tf_cache_dir'			,						type=UpperCAmelCase_			,						required=UpperCAmelCase_			,						help='Directory in which to save tensorflow model'       )
			A__	  =     parser.parse_args(UpperCAmelCase_       )

			A__	  =     BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name			,						state_dict=torch.load(args.pytorch_model_path       )			,						cache_dir=args.cache_dir			,						)

			convert_pytorch_checkpoint_to_tf(model=UpperCAmelCase_			,						ckpt_dir=args.tf_cache_dir			,						model_name=args.model_name       )


if __name__ == "__main__":
							main()






"""simple docstring"""


def 						lowerCamelCase__      (				UpperCAmelCase_ = 60_08_51_47_51_43					)->				int:







       """simple docstring"""

       try:
              UpperCamelCase       			=		int(UpperCAmelCase_					)
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."					)
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."					)
       UpperCamelCase       			=		2
       UpperCamelCase       			=		0
       if n == 2:
              return 2
       while n > 2:
              while n % i != 0:
                     i += 1
              UpperCamelCase       			=		i
              while n % i == 0:
                     UpperCamelCase       			=		n // i
              i += 1
       return int(UpperCAmelCase_					)


if __name__ == "__main__":
      print(F'''{solution() = }''')





